{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DDC-TTS_and_MultiBand-MelGAN_Example.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LWsNd3_M3MP",
        "colab_type": "text"
      },
      "source": [
        "# Mozilla TTS on CPU Real-Time Speech Synthesis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAqrSIWgLyP0",
        "colab_type": "text"
      },
      "source": [
        "We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n",
        "\n",
        "Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n",
        "\n",
        "MultiBand-Melgan is trained  1.45M steps with real spectrograms.\n",
        "\n",
        "Note that both model performances can be improved with more training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ku-dA4DKoeXk",
        "colab_type": "text"
      },
      "source": [
        "### Download Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGIgnWhGsxU1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "88725e41-a8dc-4885-b3bf-cac939f38abe",
        "tags": []
      },
      "source": [
        "!gdown --id 1dntzjWFg7ufWaTaFy80nRz-Tu02xWZos -O data/tts_model.pth.tar\n",
        "!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dnpE0-kvTsu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "76377c6d-789c-4995-ba00-a21a6e1c401e",
        "tags": []
      },
      "source": [
        "!gdown --id 1Ty5DZdOc0F7OTGj9oJThYbL5iVu_2G0K -O data/vocoder_model.pth.tar\n",
        "!gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/config_vocoder.json\n",
        "!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zlgi8fPdpRF0",
        "colab_type": "text"
      },
      "source": [
        "### Define TTS function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-Yc42nQZG5A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n",
        "    t_1 = time.time()\n",
        "    waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n",
        "                                                                             truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\n",
        "    # mel_postnet_spec = ap.denormalize(mel_postnet_spec.T)\n",
        "    if not use_gl:\n",
        "        waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
        "        waveform = waveform.flatten()\n",
        "    if use_cuda:\n",
        "        waveform = waveform.cpu()\n",
        "    waveform = waveform.numpy()\n",
        "    rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
        "    tps = (time.time() - t_1) / len(waveform)\n",
        "    print(waveform.shape)\n",
        "    print(\" > Run-time: {}\".format(time.time() - t_1))\n",
        "    print(\" > Real-time factor: {}\".format(rtf))\n",
        "    print(\" > Time per step: {}\".format(tps))\n",
        "    IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate']))  \n",
        "    return alignment, mel_postnet_spec, stop_tokens, waveform"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZksegYQepkFg",
        "colab_type": "text"
      },
      "source": [
        "### Load Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVa0kOamprgj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import torch\n",
        "import time\n",
        "import IPython\n",
        "\n",
        "from TTS.tts.utils.generic_utils import setup_model\n",
        "from TTS.utils.io import load_config\n",
        "from TTS.tts.utils.text.symbols import symbols, phonemes\n",
        "from TTS.utils.audio import AudioProcessor\n",
        "from TTS.tts.utils.synthesis import synthesis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EY-sHVO8IFSH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# runtime settings\n",
        "use_cuda = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1aIUp2FpxOQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model paths\n",
        "TTS_MODEL = \"data/tts_model.pth.tar\"\n",
        "TTS_CONFIG = \"data/config.json\"\n",
        "VOCODER_MODEL = \"data/vocoder_model.pth.tar\"\n",
        "VOCODER_CONFIG = \"data/config_vocoder.json\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpgmdBVQplbv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load configs\n",
        "TTS_CONFIG = load_config(TTS_CONFIG)\n",
        "VOCODER_CONFIG = load_config(VOCODER_CONFIG)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmrQxiozIUVE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "outputId": "60c4daa0-4c5b-4a2e-fe0d-be437d003a49",
        "tags": []
      },
      "source": [
        "# load the audio processor\n",
        "TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n",
        "ap = AudioProcessor(**TTS_CONFIG.audio)         "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fLoI4ipqMeS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b789066e-e305-42ad-b3ca-eba8d9267382",
        "tags": []
      },
      "source": [
        "# LOAD TTS MODEL\n",
        "# multi speaker \n",
        "speaker_id = None\n",
        "speakers = []\n",
        "\n",
        "# load the model\n",
        "num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n",
        "model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n",
        "\n",
        "# load model state\n",
        "cp =  torch.load(TTS_MODEL, map_location=torch.device('cpu'))\n",
        "\n",
        "# load the model\n",
        "model.load_state_dict(cp['model'])\n",
        "if use_cuda:\n",
        "    model.cuda()\n",
        "model.eval()\n",
        "\n",
        "# set model stepsize\n",
        "if 'r' in cp:\n",
        "    model.decoder.set_r(cp['r'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKoq0GgzqzhQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "234efc61-f37a-40bc-95a3-b51896018ccb",
        "tags": []
      },
      "source": [
        "from TTS.vocoder.utils.generic_utils import setup_generator\n",
        "\n",
        "# LOAD VOCODER MODEL\n",
        "vocoder_model = setup_generator(VOCODER_CONFIG)\n",
        "vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location=\"cpu\")[\"model\"])\n",
        "vocoder_model.remove_weight_norm()\n",
        "vocoder_model.inference_padding = 0\n",
        "\n",
        "ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio'])    \n",
        "if use_cuda:\n",
        "    vocoder_model.cuda()\n",
        "vocoder_model.eval()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ws_YkPKsLgo-",
        "colab_type": "text"
      },
      "source": [
        "## Run Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuWxZ9Ey5Puj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "9c06adad-5451-4393-89a1-a2e7dc39ab91",
        "tags": []
      },
      "source": [
        "sentence =  \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n",
        "align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, use_cuda, ap, use_gl=False, figures=True)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}